Current Dynamic Models Research Articles

Dynamic modeling and analysis of compressed air energy storage for multi-scenario regulation requirements

Compressed air energy storage (CAES) technology has received widespread attention due to its advantages of large scale, low cost and less pollution. However, only mechanical and thermal dynamics are considered in the current dynamic models of the CAES system. The modeling approaches are relatively homogeneous. CAES power stations have gradually increased the demand for auxiliary services such as frequency modulation mode and voltage regulation mode in addition to the peak regulation mode. Therefore, it is urgent to pay attention to the dynamic response characteristics at shorter time scales. Due to the lack of an accurate and complete dynamic model of the CAES system for multi-scenario adaptation requirements, the development of system optimization design and control technology has been severely limited. The paper establishes a dynamic model of advanced adiabatic compressed air energy storage (AA-CAES) considering multi-timescale dynamic characteristics, interaction of variable operating conditions and multivariate coordinated control. The simulation data is compared with the measured data of the peak regulation, frequency regulation and voltage regulation scenarios of the Jintan Salt Cave CAES (JTSC-CAES). The results show that the deviation of the dynamic model is <7 %, which verifies the validity and accuracy of the proposed AA-CAES dynamic model. The established model and research results provide the theoretical model foundation for dynamic characteristic research, system design and optimization control of the JTSC-CAES.

Earlier peak photosynthesis timing potentially escalates global wildfires.

More intense fire weather due to climate change is implicated as a key driver of recent extreme wildfire events. As fuel stock, the role of vegetation and its phenology changes in wildfire dynamics, however is not fully appreciated. Using long-term satellite-based burned areas and photosynthesis observations, we reveal that an earlier peak photosynthesis timing (PPT) potentially acts to escalate subsequent wildfires, with an increase in the global average burned fraction of 0.021% (∼2.20 Mha) for every additional day of PPT advancement. Satellite observations and Earth System modeling consistently show that this fire escalation is likely due to intensified drought conditions and increased fuel availability associated with the climate feedback arising from earlier PPT. Current fire-enabled dynamic global vegetation models can reproduce the observed negative correlation between PPT and burned area but underestimate the strength of the relationship notably. Given the continued PPT advancement owing to climate change, the bioclimatic effects of vegetation phenology change suggest a potentially pervasive upward pressure on future wildfires.

A method of predicting and managing public opinion on social media: An agent-based simulation

In current opinion dynamics models for predicting public opinion, the spread of events within social media has been inadequately considered, resulting in suboptimal prediction performance and inefficient strategies for public opinion management. This deficiency is particularly consequential for governments and enterprises, as adverse public opinions associated with them can inflict significant harm. This study develops a link prediction-based opinion dynamics (LPOD) model to address this gap in predicting and managing public opinion. The proposed model integrates insights from epidemiology, specifically the susceptible-infected-recovered model, to characterize the spread of events. The LPOD model enhances the updating process of relationships and opinions by redesigning the link and opinion prediction methods. Subsequently, a link-recommendation-based management approach is formulated to manage public opinion effectively. Experimental results reveal that, compared to existing models, the proposed model elevates opinion prediction accuracy from 0.81 to 0.92 and link prediction accuracy from 0.60 to 0.70. In terms of public opinion management efficacy, when compared to conventional methods such as managing opinion leaders and introducing particular nodes in social networks, the developed approach demonstrates a 20% and 18% increase in success rates, respectively. Furthermore, validation through simulations and real-world scenarios robustly confirms the model's versatile applicability and effectiveness.

Dynamic Characterization of SynRM With Dual-Axis Hybrid Excitation Self-Commissioning

The synchronous reluctance machine (SynRM) has gained increasing attraction for future electric drive systems. The characterization of its magnetic and current dynamics is essential for high-performance model-driven control, which still remains a promising problem. In this paper, a dynamic characterization scheme for SynRM with dual axis hybrid excitation self-comissioning is proposed to characterize the magnetic and current dynamics. Inspired by separating dual-axis current variables, a compact magnetic circuit model is investigated with reciprocity analytics. Furthermore, an improved current dynamic model is derived with resulting analytical apparent and incremental inductances. On this basis, the characterized parameters involved in the magnetic circuit and current dynamic models can be accurately identified by combining the designed dual axis hybrid excitation method with effective parameter identification algorithm. The effectiveness and practicability of the proposed scheme are verified based on the development platform for commercial SynRM drive. This simple dynamic characterization scheme fits for online implementation on standard drives. Compared with the existed work, 38.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvement of normalized modeling accuracy for magnetic circuit can be achieved. In the meanwhile, the normalized modeling errors of apparent inductances for current dynamics are strictly less than 6.91 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Dynamical Model of Rotation and Orbital Coupling for Deimos

This paper introduces a novel dynamical model, building upon the existing dynamical model for Deimos in the current numerical ephemerides, which only encompasses the simple libration effects of Deimos. The study comprehensively incorporates the rotational dynamics of Deimos influenced by the torque exerted by the major celestial bodies (Mars, the Sun) in the solar system within the inertial space. Consequently, a full dynamical model is formulated to account for the complete coupling between the rotation and orbit of Deimos. Simultaneously, employing precision orbit determination methods used for artificial satellites, we develop an adjustment model for fitting data to the complete model. The 12-order Adams–Bashforth–Moulton (ABM) integration algorithm is employed to synchronously integrate the 12 state variables of the full model to obtain the orbit of Deimos.The difference in the orbits obtained by integrating the full model over a period of 10 years and those obtained by the simplified model is at the order of 10 km. After precise orbit determination, this difference decreases to below 100 m, so numerical simulation results indicate that the full dynamical model and adjustment model are stable and reliable. Simultaneously, the integration of the Deimos third-order gravity field in the full model over a 10-year period induces only meter-level positional changes. This suggests that when constructing the complete model, the utilization of a second-order gravity field alone is sufficient. Compared to the simple model, the polar axis of Deimos in the inertial space exhibits a more complex oscillation in the full model. Additionally, the full model calculates that the minimum moment of inertia principal axis of Phobos has an amplitude of approximately 0.5 degrees in the longitude direction and does not exceed 2 degrees in the latitude direction. This work further advances the current dynamical model for Deimos and establishes the foundational model for the generation of a new set of precise numerical ephemerides for Deimos.

Seasonal predictability of SST anomalies and marine heatwaves over the Kuroshio extension region in the Copernicus C3S models

The warm sea surface temperature anomalies (SSTAs) and marine heatwaves (MHWs) in the Kuroshio Extension (KE) region have profound impacts on local and surrounding ecological and climatic systems. This study evaluates the seasonal prediction skills of KE-SSTAs and KE-MHWs based on six dynamical models from the Copernicus Climate Change Service (C3S) using different observational datasets for verification and further investigates the main sources of predictability. The results show that current dynamical models can provide reliable predictions for KE-SSTAs for up to about 4 months, but they are challenging to accurately predict the occurrence of KE-MHWs. Compared with single models, the C3S multi-model ensemble mean is usually more skillful in predicting KE-SSTAs and KE-MHWs at most lead times. With lead time increasing, the dynamical models tend to underestimate the mean intensity and annual frequency of the KE-MHWs and overestimate their mean duration. The performance of models in predicting KE-SSTAs is largely dependent on their ability to predict the Pacific Decadal Oscillation, Interdecadal Pacific Oscillation, and El Niño–Southern Oscillation which all significantly influence the KE-SSTAs. The results indicate that these three climate modes are the main sources of seasonal predictability for KE-SSTAs and KE-MHWs. These results provide a deeper understanding of the dynamical seasonal predictability of SSTAs and MHWs in the KE region.

A neural probabilistic bounded confidence model for opinion dynamics on social networks

Opinion dynamics (OD) models simulate the evolution of personal opinions using computer science, social dynamics, physics, and complexity science tools on social networks. However, current opinion dynamics models mainly utilize agent-based models to simulate user interactions but still need to leverage large-scale data fully. Social networks typically involve a vast number of users and information, making it difficult for models to capture the complex interactions in real social networks. Additionally, many models overlook the specific mechanisms of user interactions, such as how network individuals influence and interact with one another, resulting in inaccuracies in modeling individual opinion evolution. This paper proposes a novel approach that integrates the probabilistic bounded confidence model (PBC) with deep learning, i.e., Neural Probabilistic Bounded Confidence (NPBC), to tackle these challenges. The NPBC model adopts an attention network, a long short-term memory (LSTM) network, and a feedforward neural network to approximate the evolution of individual opinions within networks. It satisfies the data approximation and the users’ opinion update rule that conforms to the PBC model. This model enhances the understanding of opinion dynamics, helping to develop effective strategies for opinion guidance in social networks. Finally, the proposed NPBC model is evaluated on three synthetic and Twitter datasets. The results demonstrate that the proposed NPBC model performs better than the six baseline methods in predicting user opinions. Compared to baseline models, the model’s accuracy (ACC) and comprehensive evaluation (F1) scores improve by up to 18% and 7%, respectively.

A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery

Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge.

An Examination of the Stiffness Terms Needed to Model the Dynamics of an Eddy Current Based Maglev Vehicle

This paper re-examines the basis for each eddy current stiffness term computed from prior published steady-state eddy current models. The paper corrects prior analysis work by confirming, through the use of 2-D and 3-D dynamic finite element analysis modelling, that when a magnetic source is moving over an infinite-wide and infinite-long conductive sheet guideway the steady-state lateral and translational stiffness terms will be zero and only the vertical coupled stiffness terms need to be modelled. Using these observations, a much simplified 6 degrees-of-freedom (DoF) linearized eddy current dynamic force model can be used to compute the steady-state force changes in eddy current based maglev vehicles when operating over a wide uniform conductive track.

CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation

AbstractDeep learning (DL) has achieved notable success in El Niño‐Southern Oscillation (ENSO) forecasts. Most DL‐based models focused on forecasting ENSO indices while the zonal distribution of sea surface temperature anomalies (SSTA) over the equatorial Pacific was overlooked. To provide accurate predictions for the SSTA zonal pattern, this study developed a model through leveraging the merits of the cosine distance in constructing the convolutional neural network. This model can skillfully predict the SSTA zonal pattern over the equatorial Pacific 1 year in advance, remarkably outperforming current dynamical models. Moreover, the physical interpretation of the model prediction reveals that the sources for ENSO predictability at different lead times are distinct. For the 10‐month‐lead predictions, the precursors in the north Pacific, south Pacific and tropical Atlantic play critical roles in determining the model behaviors; while for the 16‐month‐lead predictions, the initial signals in the tropical Pacific associated with the discharge‐recharge cycle are essential.

An elastoplastic damage constitutive model for capturing dynamic enhancement effect of rock and concrete through equivalent stress history

The strength enhancement effect of rock and concrete under dynamic loading is one of the significant response characteristics differing from quasi-static loading. Typically, current dynamic constitutive models describe this phenomenon by adopting strain/stress rate dependent functions. In this study, a novel elastoplastic damage constitutive model that captures the dynamic strength increase through the equivalent stress history is developed by coupling the incubation characteristic time (ICT) criterion and the unified strength theory (UST). Specifically, the ICT criterion substitutes the stress history for the strain/stress rate to predict dynamic yield strength of rock and concrete. And the equivalent stress expressed by the UST provide an appropriate selection of the stress history in the ICT criterion under complex stress states. Furthermore, based on the non-associated plastic flow rule and the plastic-damage evolution law, combined with the equation of state, the proposed constitutive model is established and implemented numerically. The dynamic stress-strain curves of granite were reconstructed by a series of numerical tests on a single element, which verified the accuracy of the proposed constitutive model and the reliability of the numerical algorithm by comparing the experimental data. The proposed model was applied to the numerical simulation of dynamic compression-shear tests on inclined sandstone specimens loading by the split Hopkinson pressure bar (SHPB) and fragmentation experiments of single borehole granite specimens under blast loading, respectively. And the experimental dynamic crack patterns on the specimen surface captured by high-speed cameras match the simulated damage distribution well. In addition, several common concrete constitutive models and the proposed model were employed to simulate the dynamic response of reinforced concrete beams under near-field blast loading. The damage patterns and the mid-span residual displacement calculated numerically by the proposed model are more consistent with the experiments than other concrete models. As a conclusion, the proposed model, coupled the ICT criterion and the UST, provides a new perspective and paradigm for characterizing the responses of rock and concrete subjected to intense dynamic loads.

A semi-analytical dynamics method for spindle radial throw in boring process

In precision boring processing, accurate prediction of the radial throw of a boring tool spindle has critical significance for reducing machining errors. However, the classical dynamics method of radial throw based on the rigid body partition method ignores special characteristics of the boring tool spindle, resulting in additional errors regarding radial throw evaluation. Hence, a semi-analytical dynamics method considering the axial length, variable cross-section, and offset distance was proposed to decrease the computational error of the current dynamics model. The mechanism of radial throw was analyzed by the mechanics of materials and mathematical formulas. The Euler-Bernoulli beam theory and partial differential equation method were utilized to derive the dynamics model of the boring tool spindle, and an improved Galerkin variational algorithm was used to resolve the dynamics formulas for the radial throw. Then, the proposed dynamics method was compared with the non-contact displacement measurement and the traditional lumped mass parameter method. The validated results showed that the presented dynamics method had good agreement with the experimental data, and the maximum predictive error was 16.08 μm lower based on the proposed method. Furthermore, the machining precision of holes was also clearly evaluated and discussed using the proposed dynamics method under different processing conditions, and the lower uncertainty of the proposed dynamics method was also demonstrated. As a result, the proposed semi-analytical dynamics method can accurately compute the radial throw and evaluate the machining error in boring technology.

Construction of deep-learning based WWBs parameterization for ENSO prediction

Westerly wind bursts (WWBs) significantly impact the occurrence and development of the El Niño-Southern Oscillation (ENSO). Current dynamical models, however, face significant challenges in representing WWBs. In this study, deep learning techniques were used to develop a new parameterization scheme for WWBs and further compared against two widely used schemes. The results show that the scheme developed in this study has greater capability than previous schemes in reproducing WWBs characteristics, particularly in terms of occurrence probability, location, and duration. This improvement was mainly reflected in El Niño years, especially in strong events when the deep-learning-based scheme much realistically captures the location and strength of WWBs. It is expected that the new parameterization scheme will further improve ENSO prediction in dynamical models.

Dimensional discussion of traction force vector on ball/raceway interface and study of bearing dynamic behavior

Abstract As a crucial component, rolling bearings directly determine the reliability of rotating equipment. However, current dynamic models for predicting the bearing performance either ignore the velocity and stress dispersion at the ball/raceway interface or fail to consider the spin moment generated within the interface. To address this issue, the discrete features of the velocity and stress distribution are considered in this paper, and the micro-element approach is used to construct formulas to obtain the traction vectors in two and three dimensions, respectively. Two bearing dynamic models are further developed for these two types of equations: one model considers the spin moment at the interface owing to unequal contact angles between the ball and the two raceways, while the other model ignores this moment. The reliability of these models is validated by comparison with experimental test results, including cage speed and oil film thickness. The predictions from the quasi-static model are used as theoretical values to compare the ability of the two models to simulate bearing performance under different operating conditions. The results show that the prediction results of the model considering the spin moment are closer to the theoretical values than those of the model ignoring this moment. However, the moment increases the friction at the ball/raceway interface, causing this model to underestimate the extent of bearing sliding.

A New Cascaded Adaptive Deadbeat Control Method for PMSM Drive

In this article, we propose a novel cascaded adaptive deadbeat (CADB) control method for permanent magnet synchronous motor drives. First, an adaptive deadbeat (DB) based current controller is proposed with a simplified first-order current loop dynamic model. The motor parameters are compressed into few identifiable coefficients, and an improved gradient method with an adjustable gain factor is employed to identify these time-varying coefficients. Therefore, there is no need to design an extra observer to obtain specific motor physical parameters. Next, on the basis of the differential equation solving method, a similar model of speed loop is presented and adopted for the design of an adaptive speed controller. A robust DB-based tracking control law and a feedback control law are applied to the proposed adaptive controller. The stability of the proposed adaptive controller is confirmed by using the Lyapunov theorem. Finally, the proposed CADB control system is experimentally carried out in the steady state and transient state. The test results indicate that the system has good dynamic performance and robustness to the disturbance of parameters and loads.

A Generalized Bearing Dynamic with Adaptive Variation of Equation Numbers and Sliding Behavior Investigation

The complex sliding behavior inside ball bearings seriously affects the mechanical system’s performance. Current dynamic models for predicting this behavior suffer from poor generality and convergence. To address this issue, different interactions between the ball and raceway are proposed in this paper to simulate the dynamic behavior by analyzing the bearing assemblies’ motion features under typical operating conditions. The number of variables and equations to be solved is determined adaptively according to the bearing load characteristics, thus improving the efficiency and convergence of the model solution. The good agreement between simulation results and experimental test results validates the reliability of the model. The sliding behavior at the ball/raceway interface under different conditions is further investigated. The results show that the heavy external loads can avoid severe sliding at the interface but shorten the bearing’s fatigue. When the bearing is subjected to combined load conditions, the increased radial force inhibits bearing sliding while increasing the non-uniformity of the sliding velocity distribution.

Tertiary current distribution of a chlorine-evolving electrochemical system: A comprehensive computational study

The present study developed a tertiary current distribution computational fluid dynamics (CFD) model in a narrow-gap chlor-alkali electrochemical reactor using a two-phase model (Euler-Euler) to consider the gas-liquid hydrodynamic. Namely, the effect of bubbles on the hydrodynamic, mass transport, electrochemical process, and the chlorine-producing electrochemical reactor's performance was studied. To ensure the validity of this study, the I-V curve and cell Ohmic resistance were compared with the experimental work results, which indicated a maximum error of less than 3%, and a good agreement was obtained. It was found that considering electrolyte concentration variation significantly affects the current distribution in the gas-evolving electrochemical reactor, and tertiary current distribution study should be considered to analyze such systems. In addition, bubbles increase the operating electrical potential by about 0.6% at the average current density of 3350 A/m2. Finally, a parameter study showed that with an increase in the average current density from 1500 to 4100 A/m2, the current distribution becomes more non-uniform, where the current density variation from the bottom to the top of the electrode increase from 3% to 34% ((jmax-jmin)/javerage). Besides, raising the electrolyte inlet velocity from 0.08 to 0.22 m/s and inlet electrolyte concentration from 210 g/L (3.6 M) to 310 g/L (5.3 M) could mitigate the difference in the current density of the bottom and top of the electrode from 33% and 28% to 5% and 9%, respectively.

Applicability of Dynamic Inflow Models of HAWT in Yawed Flow Conditions

Horizontal axis wind turbines (HAWTs) experience yaw misalignments due to the physical limitations of yaw controllers and various novel active yaw controls. Moreover, the motion of floating offshore wind turbines (FOWTs) accelerates yaw misalignment. The blade element momentum (BEM) method is widely used due to its computational efficiency for the design of HAWTs. Momentum theory, the basis of BEM, assumes steady flow and uniform induction field at the disc. Those assumptions are relaxed by engineering models to capture yaw and unsteady effects. Current yaw engineering models, however, are inaccurate since they do not capture the asymmetric wake expansion effect. Dynamic inflow models have been developed for non-yawed flow. Furthermore, the AVATAR project shows that BEM using fully coupled engineering models, the current yaw, dynamic inflow and various engineering models, suffers from significant deficiencies. This purpose of this paper, therefore, is to investigate dynamic effects for yawed flow, and determine if current dynamic inflow models are applicable in yawed conditions. The Glauert’s modified momentum theory is applied to dynamic inflow models to couple the two models. Among all coupled models, Øye, Yu PWVM and Yu FWVM DIM can capture asymmetric trends. However, the results show the significant deficiencies in phase delay on the actuator disc.

Using a Simple Representation of Non‐Structural Carbohydrates Allocation to Predict Wood Growth in Temperate Forests

AbstractWood growth is an important process of carbon sequestration and plant physiology in forest ecosystems. Hence, understanding and predicting wood growth is central to quantifying forest carbon cycling. Current dynamic global vegetation models (DGVMs) are mainly driven by carbon inputs (e.g., Gross Primary Production, GPP). However, observations indicate that wood growth is often independent of carbon flux at the annual scale. Here, we revised a DGVM, FORCCHN2 model, to use the active non‐structural carbohydrates (NSC) pool and slow NSC pool (i.e., represented temporary and long‐term NSC storage, respectively), and to integrate the stored NSC with annual wood growth. For the diameter increments (ΔDBH) and aboveground wood growth of 506 trees in 32 plots of Harvard Forest, we tested the NSC allocation coefficients from 5% to 95%. The predictions reproduced 31.2%–55.1% of individual‐tree ΔDBH and 36.8%–43.0% of the aboveground wood increment. Trees of shade‐intolerant species invested more of their available carbon resources (i.e., NSC storage) into wood growth than shade‐tolerant species. Specifically, the shade‐tolerant trees consumed approximately 32% NSC storage by wood growth at the annual scale, while the shade‐intolerant trees consumed about 90%. Our study provides a simple framework that constructs a direct link between annual wood growth and NSC dynamics. The findings highlight the need for research into NSC storage and allocation in trees, particularly in considering NSC allocation strategies of different tree species.

Three-Phase Model-Based Predictive Control Methods With Reduced Calculation Burden for Modular Multilevel Converters

Model predictive control (MPC) has been widely investigated in modular multilevel converters (MMCs) due to its superiority in achieving multiple control objectives. The three-phase model-based MPC, which contains the common-mode voltage in the output current dynamic model and considers interaction among phases, shows better performance than the conventional per-phase model-based predictive control in a three-phase MMC system. However, it suffers from a heavy computational burden as the number of submodules (SMs) increases. To address this issue, this article first analyzes the relationship among the numbers of inserted SMs, the controllability of dc-link current, and circulating currents. Then, according to this analysis, two simplified MPC methods based on the three-phase model with reduced computational burden are proposed. Specifically, fewer insertion index combinations are selected in advance to ensure good output currents, controllable dc-link, and circulating currents. The effectiveness of the proposed methods is verified through experimental results.